class_docstr: SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences"
  from the <https://arxiv.org/pdf/1904.01416.pdf> paper, 
  containing about 21 lidar scan sequences with dense point-wise annotations.
  
  root dir should be structured as
  rootdir
      └── sequences/
          ├── 00/
          │   ├── labels/
          │   │     ├ 000000.label
          │   │     └ 000001.label
          │   └── velodyne/
          │         ├ 000000.bin
          │         └ 000001.bin
          ├── 01/
          ├── 02/
          .
          .
          .
          └── 21/
  Args:
      root (string): Root directory of the dataset.
      split (string, optional): If :obj:`"train"`, loads the training
          dataset.
          If :obj:`"val"`, loads the validation dataset.
          If :obj:`"trainval"`, loads the training and validation dataset.
          If :obj:`"test"`, loads the test dataset.
          (default: :obj:`"trainval"`)
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.Data` object and returns a transformed
          version. The data object will be transformed before every access.
          (default: :obj:`None`)
code: def get(self, idx):
      data = torch.load(self._scans[idx])
      if data.y is not None:
          data.y = self._remap_labels(data.y)
      return data
  
imports: import os
  from glob import glob
  import numpy
  import multiprocessing
  import logging
  import torch
  from torch_geometric.data import Dataset, Data
  from torch_points3d.datasets.base_dataset import BaseDataset
  from torch_points3d.datasets.segmentation.kitti_config import *
  from torch_points3d.datasets.segmentation import IGNORE_LABEL
  from torch_points3d.metrics.segmentation_tracker import SegmentationTracker
label_desc: Use image node_id 7 for calling the SemanticKitti obj's underlying member method code with example usage: obj.get(idx) and returns: data